Theorizing in ecology and evolution often proceeds via the construction of multiple idealized models. To determine whether a theoretical result actually depends on core features of the models and is not an artifact of simplifying assumptions, theorists have developed the technique of robustness analysis, the examination of multiple models looking for common predictions. A striking example of robustness analysis in ecology is the discovery of the Volterra Principle, which describes the effect of general biocides in predator-prey systems. This paper details (...) the discovery of the Volterra Principle and the demonstration of its robustness. It considers the classical ecology literature on robustness and introduces two individual-based models of predation, which are used to further analyze the Volterra Principle. The paper also introduces a distinction between parameter robustness, structural robustness, and representational robustness, and demonstrates that the Volterra Principle exhibits all three kinds of robustness. *Received September 2006; revised May 2007. ‡Earlier versions of this paper were presented at the Australasian Association of Philosophy, the London School of Economics, and the University of Bristol. The authors wish to thank those audiences as well as Patrick Forber, Ken Waters, Deena Skolnick Weisberg, Uri Wilensky, and Bill Wimsatt for many helpful comments. Special thanks to Giacomo Sillari for his assistance in translating Volterra's original paper and his insightful thoughts about Volterra's aims and methods. Some of the research in this paper was supported by NSF grant SES-0620887 to MW. †To contact the authors, please write to: Michael Weisberg, Department of Philosophy, University of Pennsylvania, 433 Logan Hall, Philadelphia, PA 19104; e-mail: [email protected]; Kenneth Reisman, Pluribo, Inc., 100 Park Avenue, Suite 1600, New York, NY 10017; e-mail: [email protected]. (shrink)

Abstract‘Computational thinking’ (CT) is highlighted in research literature, societal debates, and educational policies alike as being of prime significance in the 21st century. It is currently being introduced into K–12 (primary and secondary education) curricula around the world. However, there is no consensus on what exactly CT consists of, which skills it involves, and how it relates to programming. This article pinpoints four competing claims as to what constitutes the defining traits of CT. For each of the four claims, inherent (...) philosophical presuppositions are identified concerning the nature of thinking, problem-solving, and human–computer relationships. It is argued that most of these philosophical presuppositions are very similar to ones that Hubert Dreyfus and Donald Schön addressed forty to fifty years ago. Therefore, claims about the power of CT raise old discussions in a new disguise. Dreyfus and Schön argued that the presuppositions were wrong. Do their arguments hold and thus defeat contemporary claims about CT? Alternatively, has the technological development since the time of their writings proven them wrong? This article argues that it is necessary to heed the arguments of Dreyfus, Schön, and—later—Bent Flyvbjerg to ensure that CT curricula are built in accord with the value-rational nature of human practice, rather than on misconceived rationalizations of problem-solving, computer use, and computational aspects in the world. However, the pervasive integration of information technology in today's world raises new issues concerning human–machine collaborations that sidetrack the arguments of Dreyfus and Schön. A revised view of CT is required which focusses on articulating how humans can design for, partake in, and critically appraise human–machine collaborations. (shrink)

This paper takes an embodied and extended cognition perspective to ER integration – a cognitive process through which a learner integrates external representations (ERs) in a domain, with her internal (mental) model, as she interacts with, uses, understands and transforms between those ERs. In the paper, I argue for a theoretical as well as empirical shift in future investigations of ER integration, by proposing a model of cognitive mechanisms underlying the process, based on recent advances in extended and embodied cognition. (...) I present this new model in contrast to the still dominant classical cognitivist (information processing) approaches to ER integration, and the educational technology intervention designs such approaches inspire. I then exemplify this distinction between the information processing model and the new model through a case of arithmetic problem solving. Corroborative neuroscience evidence presented in relation to this case provides empirical support for the new model by showing how bodily actions (sensorimotor mechanisms) are critical to ER integration and learning. Finally, as educational implications of the new model, I demonstrate the need for: (i) re-viewing the development of ER integration and expertise as fine-tuning of the learner's action or sensorimotor system, and (ii) a shift of focus in new-media intervention design principles based on this newer understanding of ER integration in science, technology, engineering and mathematics. (shrink)

The purpose of this study was to investigate the features of modeling-based abductive reasoning as a disciplinary practice of inquiry in the domain of earth science. The study was based on an undergraduate course of a university of education, Korea, offered for preservice elementary teachers majoring in science as their specialty. The course enrollees participated in an inquiry project in which they were asked to abductively generate models representing past geologic events in order to explain how two units in a (...) sedimentary rock outcrop had been formed. Three students were selected as major informants for the study, and multiple types of qualitative data were collected, including the students’ sketchbook records, audio-recording of whole-class presentation and discussion, and interviews with the students. The data were analyzed according to the method of analytical induction, which yielded three assertions as the findings of the study. First, while an explanatory model can be generated by combining resource models, the combination of resource models does not necessarily result in a scientifically sound explanatory model. Second, a systemic approach can help activate a critical resource model which can in turn lead to a scientifically sound explanatory model. Third, simulations with a model can enhance the plausibility of the model. Based on these findings, causal combinations of resource models, a systemic approach to earth scientific problem-solving, and simulations with a model are suggested and discussed as implications for science education and relevant research. (shrink)

Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

Studies of scientific practice demonstrate that the development of scientific models is an enactive and emergent process. Scientists make meaning through processes such as perspective taking, finding patterns, and following intuitions. In this paper, we focus on how a group of fourth grade learners and their teacher engaged in interpretation in ways that align with core ideas and practices in kinematics and computing. Cycles of measuring and modeling––including computer programming––helped to support classroom interactions that highlighted the interpretive nature of modeling (...) and participation in model construction as a knowledge-building process. We draw on literature from the history and philosophy of science in order to analyze the students’ interpretive actions as forms of epistemic and representational agency, constituting a construct we term disciplined interpretation. We demonstrate how students’ disciplined interpretative moves help to position them as owners of their own design decisions and their rights to interpret the phenomena they were modeling, data collected from those phenomena, and the scientific and computational models themselves. We present four extended episodes that characterize the nature of activity in the classroom and the development of students’ disciplined interpretations in terms of learning to recognize scientific patterns amid complex perceptual fields, and to represent them in ways that support sensemaking. (shrink)